## ECONet: Efficient Convolutional Online Likelihood Network for Scribble-based Interactive Segmentation

10 Dec 2021, 23:35 (edited 22 Jun 2022)MIDL 2022Readers: Everyone
• Keywords: interactive segmentation, online learning, neural networks, COVID-19
• TL;DR: We propose an efficient convolutional neural networks (CNNs) that can be learned online while the annotator provides scribble-based interaction.
• Abstract: Automatic segmentation of lung lesions associated with COVID-19 in CT images requires large amount of annotated volumes. Annotations mandate expert knowledge and are time-intensive to obtain through fully manual segmentation methods. Additionally, lung lesions have large inter-patient variations, with some pathologies having similar visual appearance as healthy lung tissues. This poses a challenge when applying existing semi-automatic interactive segmentation techniques for data labelling. To address these challenges, we propose an efficient convolutional neural networks (CNNs) that can be learned online while the annotator provides scribble-based interaction. To accelerate learning from only the samples labelled through user-interactions, a patch-based approach is used for training the network. Moreover, we use weighted cross-entropy loss to address the class imbalance that may result from user-interactions. During online inference, the learned network is applied to the whole input volume using a fully convolutional approach. We compare our proposed method with state-of-the-art using synthetic scribbles and show that it outperforms existing methods on the task of annotating lung lesions associated with COVID-19, achieving 16% higher Dice score while reducing execution time by 3$\times$ and requiring 9000 lesser scribbles-based labelled voxels. Due to the online learning aspect, our approach adapts quickly to user input, resulting in high quality segmentation labels. Source code for ECONet is available at: https://github.com/masadcv/ECONet-MONAILabel
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• Paper Type: both
• Primary Subject Area: Segmentation
• Secondary Subject Area: Learning with Noisy Labels and Limited Data
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